The high number of unnecessary deaths in the hospital due to errors related to pharmaceutical administration such as sedative and narcotics has been a recent focus of US government studies and much discussion in the literature and press. The present inventors recognized that these adverse events occur not only due to improper dosage of medications or the administration of drug to the wrong patient, as has been recently highlighted in the medical literature and press, but also due to failure to recognize complex patterns along monitored outputs (such as those shown in FIG. 2) indicative of patient instability before, during, and after the administration of such medications. These patterns can provide evidence that a given dose of medication, which may appear to be correct according to the Physician's Desk Reference or other source, may be too much for a given patient in a given physiologic state. Administration of “standard acceptable dosages” to patients with potentially unstable physiology can produce an insidious and deadly occurrence of relative drug excess, which will not be prevented by simple computer matching of patient name and drug. Further, the present inventors recognized that failure to timely interrupt infusion upon the occurrence of physiologic instability represented a major cause of death. The timely recognition of a change in the pattern of the patient monitored output can be seen as the last opportunity to correct the mistake of wrong drug, wrong dose, wrong patient, relative drug excess, or a potentially fatal idiosyncratic or allergic reaction.
In hospitals, throughout the United States monitored patients are experiencing profound physiologic instability before and during medication infusion producing patterns as shown in FIG. 2 and yet still are being subjected to continuous infusion of further destabilizing and potentially deadly narcotics and sedation simply because the hospital monitors do not recognize the patterns nor are they programmed to warn the hospital worker or to lock out the infusion based on such recognition. An example of such instability and a system and method according to the present invention for identification of such patterns follows although, upon this teaching, the skilled artisan will recognize that there are many modifications within the scope of this teaching, which will allow the recognition of other patterns of instability
A major factor in the development of respiratory failure (one of the most common causes of death in the hospital) is airway instability, which results in airway collapse during sedation, stroke, narcotic administration, or stupor. As illustrated in FIGS. 3a and 3b, such collapse occurs in dynamic cycles called airway instability clusters affecting a range of physiologic signals. Subgroups of patients in the hospital are at considerable risk from this type of instability. In addition patients with otherwise relatively stable airways may have instability induced by sedation or narcotics. The present inventors recognized that it is critical that this instability be recognized in real time in the hospital so that the dose can be adjusted or the drug withheld upon the recognition of this development. They also realized that it is critical to use the opportunity afforded by hospitalization in association with hospital monitoring to automatically evaluate for the common disorder induced by upper airway instability—obstructive sleep apnea. Conventional central patient monitors are neither configured to provide interpretive recognition the cluster patterns indicative of airway and ventilation instability nor to provide interpretative recognition of the relationship between and along airway instability clusters. In fact, such monitors often apply averaging algorithms, which attenuate the clusters. For these reasons thousands of patients each day enter and leave hospital-monitored units with unrecognized sleep apnea and ventilation and airway instability.
This failure of conventional hospital based patient monitors to timely and/or automatically detect cluster patterns indicative of airway instability can be seen as a major health care deficiency indicative of a long unsatisfied need. Because obstructive sleep apnea, a condition derived from airway instability, is so common, the consequence of the failure of conventional hospital monitors to routinely recognize upper airway instability clusters means that many of patients with this disorder will never be diagnosed in their lifetime. For these patients, the diagnostic opportunity was missed and the health implications and risk of complications associated with undiagnosed airway instability and sleep apnea will persist in this group throughout the rest of their life. A second group of patients will have a complication in the hospital due to the failure to timely recognize airway instability. Without recognition of the inherent instability, a patient may be extubated too early after surgery or given too much narcotic (the right drug, the right patient, the ordered dose but unknowingly a “relative drug excess”). Indeed until clusters indicative of airway instability are routinely recognized by hospital monitors, the true incidence of respiratory failure, arrest, and/or death related to the administration of IV sedation and narcotics to patients in the hospital with airway instability will never be known but the number is probably in the tens of thousands each year and airway instability is just one example of the types of physiologic instability which are not automatically characterized by central hospital systems.
To understand the criticality of recognizing airway instability in real-time it is important to consider the significance of the combined effect that oxygen therapy and narcotics or sedation may have in the patient care environment in the hospital, for example, in the management of a post-operative obese patient after upper abdominal surgery. Such a patient may be at particular risk for increased airway instability in association with narcotic therapy in the through 3rd post-operative day due to sleep deprivation, airway edema, and sedation. Furthermore, in the second and third postoperative day monitoring the vigilance of hospital personnel may diminish due to perceived stability, and rebound rapid eye movement (REM) sleep which can increase upper airway instability may occur due to antecedent sleep deprivation. Indeed, many of these patients have significant sleep apnea prior to admission to the hospital which is unknown to the surgeon or the anesthesiologist due to the subtly of symptoms. Such patients, even with severe sleep apnea, are relatively safe at home because of an intact arousal response; however, in the hospital, narcotics and sedatives often remove this “safety net. The administration of post-operative narcotics can shift the arousal curve to the right and this can significantly increase the danger of airway instability and, therefore, place the patient at substantial risk. Many of these patients are placed on electrocardiographic monitoring but the alarms are generally set at high and low limits. Hypoxemia, induced by airway instability generally does not generally produce marked levels of tachycardia; therefore, airway instability is poorly identified by simple electrocardiographic monitoring without the identification of specific pattern of clusters of the pulse rate. In addition, simple oximetry evaluation is also a poor method to identify airway instability. Conventional hospital oximeters often have averaging intervals, which attenuate the dynamic desaturations. Even when the clustered desaturations occur they are often thought to represent false alarms because they are brief. When desaturations are recognized as potentially real this often results in the simple and often misguided addition of nasal oxygen. However, nasal oxygen may prolong the apneas and potentially increase functional airway instability. From a monitoring perspective, the addition of oxygen therapy can be seen to potentially hide the presence of significant airway instability by attenuation of the level of desaturation and reduction in the effectiveness of the oximeter as a monitoring tool in the diagnosis of this disorder.
Oxygen and sedatives can be seen as a deadly combination in patients with severely unstable airways since the sedatives increase the apneas and the oxygen hides them from the oximeter. For all these reasons, as will be shown, according to the present invention, it is critical to monitor patients with increased risk of airway instability for the specific monomorphic and polymorphic cluster patterns as will be discussed, during the administration of narcotics or sedatives.
Having identified, supra, the long and critical need, a discussion of the background physiology of upper airway instability will first be provided.
The central drive to breath, which is suppressed by sedatives or narcotics, basically controls two critical muscle groups. The upper airway “dilator muscles” and the diaphragm “pump muscles”. The tone of both these muscle groups must be coordinated. A fall in afferent output from the brain controller to the airway dilators results in upper airway collapse. Alternatively, a fall in afferent output to the pump muscles causes hypoventilation.
Two major factors contribute to respiratory arrest in the presence of narcotic administration and sedation. The first and most traditionally considered potential effect of narcotics or sedation is the suppression by the narcotic or sedative of the brains afferent output to pump muscle such as the diaphragm and chest wall, resulting in inadequate tidal volume and associated fall in minute ventilation and a progressive rise in carbon dioxide levels. The rise in carbon dioxide levels causes further suppression of the arousal response, therefore, potentially causing respiratory arrest. This first cause of respiratory arrest associated with sedation or narcotics has been the primary focus of previous efforts to monitor patients postoperatively for the purpose of minimization of respiratory arrests. Both oximetry and tidal CO2 monitoring have been used to attempt to identify and prevent this development. However, in the presence of oxygen administration, oximetry is a poor indicator of ventilation. In addition, patients may have a combined cause of ventilation failure induce by the presence of both upper airway instability and decreased diaphragm output as will be discussed, this complicates the output patterns of CO2 monitors making recognition of evolving respiratory failure due to hypoventilation more difficult for conventional threshold alarm based systems.
The second factor causing respiratory arrest due to narcotics or sedatives relates to depression of the brains afferent output to upper airway dilator muscles causing a reduction in upper airway tone. This reduction in airway tone results in dynamic airway instability and precipitates monomorphic cluster cycles of airway collapse and recovery associated with the arousal response as the patient engages in a recurrent and cyclic process of arousal based rescue from each airway collapse. If, despite the development of significant cluster of airway collapse, the narcotic administration or sedation is continued, this can lead to further prolongation of the apneas, progression to dangerous polymorphic desaturation, and eventual respiratory arrest. There is, therefore, a dynamic interaction between suppression of respiratory drive, which results in hypoventilation and suppression of respiratory drive, which results in upper airway instability. At any given time, a patient may have a greater degree of upper airway instability or a greater degree of hypoventilation. The relative combination of these two events will determine the patterns of the output of the monitor.
Unfortunately, this has been one of the major limitations of carbon dioxide monitoring. The patients with significant upper airway obstruction are also the same patients who develop significant hypoventilation. The upper airway obstruction may result in drop out of the nasal carbon dioxide signal due to both the upper airway obstruction, on one hand, or due to conversion from nasal to oral breathing during a recovery from the upper airway obstruction, on the other hand. Although breath by breath monitoring may show evidence of apnea, conversion from nasal to oral breathing can reduce the ability of the CO2 monitor to identify even severe hypoventilation in association with upper airway obstruction, especially if the signal is averaged or sampled at a low rate. For this reason, conventional tidal CO2 monitoring when applied with conventional monitors without out cluster pattern recognition may be least effective when applied to patients at greatest risk, that is, those patients with combined upper airway instability and hypoventilation. The present inventors recognized that this unique physiologic process of reentry of airway collapse could be exploited to provide a system and method for the recognition of the waveform patterns of airway instability. Several early embodiments are described in U.S. Pat. No. 6,223,064 (which is assigned to the present inventor, the disclosure and the entire contents of which are incorporated by reference is if completely disclosed herein). These systems and methods exploit the underlying cyclic physiologic process, which drives the perpetuation of a cluster of airway closures, to provide automatic recognition and indication of upper airway instability in real time. As discussed, the underlying cyclic process, which defines the behavior of the unstable upper airway, is associated with precipitous changes in ventilation and attendant precipitous changes in monitored parameters, which reflect and/or are induced by such ventilation changes. For example, cycling episodes of airway collapse and recovery produces sequential precipitous changes in waveform output defining analogous cluster waveforms in the time series of: oximetry derived pulse, airflow amplitude or/or tidal frequency, the oximetry SpO2, the chest wall impedance and/or motion, EKG pulse rate, and/or R to R interval, EEG (due to clustering of arousals), EMG due to clustering of motor response to arousals, systolic time intervals, and other parameters which vary with the brisk clustered cycles of apnea and recovery. EEG is readily available in the hospital as BIS monitors, according to the present invention the detection of clusters of alpha or high amplitude, mixed frequency arousals in clusters is very useful to indicate the potential presence of airway instability. According to the present invention, any one of these parameters singularly or in combination can be used in the hospital to detect either the absolute presence of airway instability or to provide evidence of probable airway instability so that hospital personnel know that additional testing should be applied.
Conventionally, in the hospital, the analysis of one or more time series datasets is widely used to characterize the behavior of physiologic systems and to identify the occurrence adverse events. One basic conventional hospital montage commonly connected to a central monitor by telemetry includes electrocardiogram (EKG), pulse oximetry, and chest wall impedance). Using this grouping of monitors, the human physiologic system produces a large array of highly interactive time series outputs, the dynamic relational configurations of which have substantial relevance when monitored over both brief and long time intervals. The present inventors recognized that multiple unique patterns of airway instability were present along the time series and that these different patterns could be identified to provide an interpretive output such a textual output and/or other alarm. In addition, the present inventors recognized that the complexity and time course variability of these patterns commonly overwhelms hospital workers so that timely intervention is often not applied, resulting in unnecessary death or patient injury. The inventors further recognized that the processed based recognition of these patterns could be used to take action in the interest of the health of the patient, such as automatically lock out narcotic or sedation medication or increase the level and/or type of ventilation support. They also recognized that combined central and satellite processing systems such as those used in the hospital based systems discussed supra, could be modified to provide such automatic recognition and to provide such output and/or take such action to improve the health care of patients such as automatically locking out a drug infusion upon the recognition of the interval development of an unstable pattern potentially indicative of an adverse drug reaction or titration of continuous positive pressure devices. The invention also provides a method of doing business to improve the sale of patient monitoring systems, CPAP, and disposable probes for use with the monitors.
According one aspect of the present invention, the recognition of sequential precipitous events or pathophysiologic patterns can be achieved by analyzing the spatial and/or temporal relationships between at least a portion of a waveform of a physiology parameter, (such as, for example, those listed supra), induced by at least a first episode of airway collapse and at least a portion of a waveform induced by at least a second episode of airway collapse. This can include the recognition of a pattern indicative of a cluster, which can compromise a high count of apneas with specified identifying features which occur within a short time interval along said waveform (such as 3 or more apneas within about 5–10 minutes) and/or can include the identification of a waveform pattern defined by closely spaced episodes of airway collapse defining waveform clusters. Further, the recognition can include the identification of a spatial and/or temporal relationship defined by waveform clusters, which are generated by closely spaced sequential apneas due to cycling upper airway collapse and recovery.
According to another aspect of the invention, the patterns of these complex interactive signals and the data sets defining path physiologic upper airway instability are characterized by organizing the time series into an ascending hierarchy of objects (which in one preferred embodiment are substantially in the time domain), ordering these objects into a relational data matrix and then recognizing the complex reciprocations across time series and across scales and by applying an expert system to that set of highly organized set of objects.
For the purpose of organizing and identifying physiologic datasets, according to the present invention a fundamental dynamic time series object is identified and characterized, which possesses a unique symmetry of scale. The inventors call this object a “physiologic reciprocation”. For the purpose of pattern recognition, according to the present invention, a physiologic reciprocation is a fundamental variation time series output generated by an organ, an organ system, and/or an entire organism, which is at least partially reversed within a specified interval. According to the present invention reciprocations, as recognized by the processor, are widely scalable across substantially all fundamental output patterns of organ function and physiologic control. The present inventors recognized that an scaleable system which recognized and analyzed reciprocations along a time series, across different scales of the time series, and between different scales of different contemporaneously derived time series, could be used to readily identify specific dynamic physiologic patterns of interaction defining both different states of disease and health. Further, the present inventors recognized that, for the purpose of processor based pattern recognition, human physiologic function (and dysfunction) can be characterized by defining and recognizing a object hierarchy of physiologic reciprocations ordered into an ascending, inheritance based relational timed data matrix.
Using the above discoveries the present inventors recognized that typical standard central hospital monitors including those with wireless capabilities (such as the system described for example U.S. Pat. No. 6,364,834) and outpatient holter type monitors can be improved to provide automatic recognition of airway instability and sleep apnea and to provide an automatic visual or audible indication of the presence of such clusters and further to provide a visual or audible output and severity of this disorder thereby rendering the timely recognition and diagnosis of upper airway instability and obstructive sleep apnea as routine and automatic in the hospital as the diagnosis of other common diseases such as hypertension.
FIG. 3a illustrates the reentry process driving the propagation of airway instability reentry clusters. The physiologic basis for these clusters has been previously described in U.S. Pat. Nos. 5,891,023 and 6,223,064 and provisional application No. 60/291,691 (the entire contents of each of which are incorporated by reference as if completely disclosed herein). This cycle is present when the airway is unstable but the patient is capable of arousal. In this situation, in the sleeping or sedated patient, upon collapse of the airway, the patient does not simply die, she rescues herself and precipitously opens the airway to recover by hypoventilation, however, if the airway instability remains after the arousal and rescue is over, the airway collapses again, only to be rescued again thereby producing a cluster of closely spaced apneas with distinct spatial, frequency and temporal waveform relationships between and within apneas wherein the physiologic process reenters again and again to produce a clustered output. According to the present invention, an airway instability cluster is comprised of a plurality (two or more) of closely spaced apneas or hypopneas but the use of 3 or more apneas is preferred. The present invention includes (but is not limited to) recognition of airway instability clusters in oxygen saturation, pulse, chest wall impedance, blood pressure, airflow (including but not limited to exhaled carbon dioxide and air temperature), nasal and oral pressure, systolic time intervals, electrocardiograph tracings (including pulse rate and R to R interval plots), timed plots of ST segment position, chest wall and/or abdominal movements (as by strain gauge, impendence, or other methods), electromyography (EMG), and electroencephalography (EEG). For all of these waveforms the basic underlying cluster pattern is similar and the same basic presently preferred cluster pattern recognition system and method, according to the present invention, can be applied to recognize them.
According to one aspect of the invention a microprocessor system is provided for the recognition of specific dynamic patterns of interaction between a plurality of corresponding and related time series, the system comprises a processor programmed to; process a first time series to produce a lower-level time series of sequential time series fragments derived from the first time series, process the lower-level time series to produce a higher-level time series comprised of sequential time series fragments from the lower-level time series, process a second time series, the second time series being related to the first time series, produce a second lower-level time series of sequential time series fragments derived from the second time series, and identify a dynamic pattern of interaction between the first time series and the second time series.
The system can be further programmed to process the lower-level time series of the second time series to; produce a higher-level time series derived from sequential time series fragments of the second lower-level time series. The system can be programmed to process a third time-series, the third time series being related to at least one of the first and the second time series, to produce a third lower-level time series of sequential time series fragments derived from said third time series. The system can be programmed to process the higher-level time series to produce a complex-level time series derived from sequential time series fragments of said higher-level time series. The time series fragments of the first and second time series can be stored in a relational database, the fragments of the higher-level time series can comprise objects, the objects inheriting the characteristics of the objects of the lower-level time series from which they are derived. The first and second time series can comprise datasets of physiologic data points and the system can comprise a patient monitoring system wherein the dynamic pattern of interaction comprises convergent clusters of pathologic reciprocations.
It is the purpose of the present invention to provide a diagnostic system, which can convert conventional hospital-based central telemetry and hard wired monitoring systems and portable home systems to provide processor based recognition of airway instability through the recognition of patterns of closely spaced reciprocations and/or events induced by apneas and/or hypopneas both in real time and in overnight interpretive format and which can automatically lock-out narcotic infusion upon recognition of patterns of instability.
It is the purpose of the present invention to provide a system, which identifies, maps, and links waveform clusters of airway instability from simultaneously derived timed signals of multiple parameters including chest wall movement, pulse, airflow, exhaled carbon dioxide, systolic time intervals, oxygen saturation, EKG-ST segment level, EEG, EMG, and other parameters to enhance the real-time and overnight diagnosis of airway instability.
It is further the purpose of the present invention to provide a system to provide a graded index and/or indication of patterns of airway instability.
It is further the purpose of the present invention to provide a system, which provides characterization of different types of patterns of ventilation and/or upper airway instability.
It is further the purpose of the present invention to provide a system, which provides characterization and/or differentiation of different types of patterns such as monomorphic, polymorphic, and combined patterns of instability.
It is further the purpose of the present invention to provide a system, which provides characterization to output an indication of the type of pattern identified by the processor so that a decision relevant the probability of success of auto titration with CPAP and/or BIPAP can be made.
It is further the purpose of the present invention to provide timely, real-time indication such as a warning or alarm of the presence of airway instability clusters so that nurses can be aware of the presence of a potentially dangerous instability of the upper airway during titration of sedatives and/or narcotics.
It is further the purpose of the present invention to provide a system for the recognition of airway instability for combined cluster mapping of a timed dataset of nasal oral pressure with tidal CO2 to identify clusters of conversion from nasal to oral breathing and to optimally recognize clusters indicative of airway instability in association with tidal CO2 measurement indicative of hypoventilation.
It is the purpose of the present invention to provide an iterative object oriented waveform processing system, which can characterize, organize, and compare multiple signal levels across a plurality of signals by dividing each waveform level of each signal into objects for discretionary comparison within a relational database, object database or object-relational database
It is further the purpose of the present invention to provide a system, which automatically triggers lockout of medication infusion based on the recognition of an adverse pattern of instability along at least one timed dataset output.
It is another aspect of the present invention to provide a system that automatically customizes treatment algorithms or diagnostic algorithms based on the analysis of waveforms of the monitored parameters.
It is further the purpose of the present invention to provide a system, which provides recognition and characterization of physiologic reciprocations across different time series scales.
It is further the purpose of the present invention to provide a system, which automatically triggers testing (and comparison of the output) of a secondary intermittently testing monitor upon the recognition of patterns indicative of physiologic instability.
It is further the purpose of the present invention to provide real time protection to patients against adverse drug and to provide a data matrix comprising matched time series of physiologic signals with a time series of drug infusion so that hospital personnel can readily match specific patterns of pathophysiologic perturbations to specific types of medications and ranges of medication dosage for patients hospital wide.